الگوریتم¬های یادگیری عمیق در فراتفکیک پذیری تصاویر
محورهای موضوعی : مهندسی مخابرات
بهار قادری
1
(
)
حمید آزاد
2
(
دانشکده مهندسی برق- واحد شیراز، دانشگاه آزاد اسلامی، شیراز، ایران
)
کلید واژه: فراتفکیک پذیری, الگوریتم¬های یادگیری عمیق, تصویر با کیفیت, شبکه عصبی کانولوشن,
چکیده مقاله :
فراتفکیک¬پذیری تصویر یکی از فرآیند¬های مهم پردازش تصویر جهت افزایش وضوح تصاویر و ویدئو¬ها می¬باشد. در سال¬های اخیر، روش¬های مبتنی بر شبکه¬های عصبی عمیق جهت فراتفکیک¬پذیری شاهد پیشرفت قابل توجهی بوده است. هدف این مقاله ارائه یک بررسی جامع در مورد پیشرفت¬های اخیر فراتفکیک¬پذیری تصویر با استفاده از رویکرد¬های یادگیری عمیق است. در اين مقاله، ضمن معرفي مفاهیم فراتفکیک¬پذیری تصویر، به بررسی الگوریتم¬های رایج یادگیری عمیق جهت فراتفکیک¬پذیری، و کاربردهای فراتفکیک¬پذیری پرداخته شده¬است. علاوه بر این، مجموعه پایگاه¬های داده و معیارهای ارزیابی تشریح داده می¬شود. اين مقاله مي¬تواند راهگشاي محققان پردازش تصوير در فرآیند فراتفکیک¬پذیری باشد. اهتمام نويسندگان بر اين بوده است که همه جنبه¬هاي اين فرآیند مورد کاوش قرار گيرد.
Image super-resolution is one of the important image processing processes to increase the resolution of images and videos. In recent years, methods based on deep neural networks for super-resolution have seen significant progress. The aim of this paper is to provide a comprehensive review on recent developments in super-resolution image using deep learning approaches. In this article, while introducing the concepts of image super-resolution, the common deep learning algorithms for super-resolution and the applications of super-resolution have been investigated. In addition, the set of databases and evaluation criteria are described. This article can open the way for image processing researchers in the super-resolution process. The authors’ effort has been to explore all aspects of this process.
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